Social and Information Networks Analysis: Problems, Models and Machine Learning Methods

IIT Hyderabad is organising a course on "Social and Information Networks Analysis: Problems, Models and Machine Learning Methods" in December 2017. The course will be held at IIT Hyderabad Campus from 11th December to 14th December 2017. The course instructor is Dr. Manuel Gomez Rodriguez, faculty at Max Planck Institute for Software Systems.

The course is being offered under the GIAN scheme launched by the Govenment of India.

News and Updates

Course slides updated. See schedule.

Course brochure is here

Course schedule updated. See here.

Last date for application: Nov 30,2017.

Route map to reach the old campus is here. Free transport via institute buses is available between the two campuses.

Route map to drive to IIT Hyderabad Permanent Campus is here. This document contains the same details, with information about public transport.

Clarification: The exam on the last day is optional. It is for those who are interested to write the exam. People who write the exam would also get a grade certificate at the end of the course.


Social and information networks have created a platform for human to share information at an unprecedented scale. It provides access to vast amount of information which is of significant value to government agencies, journalists and business companies. Mining and analysing the flow of information in social networks are of particular importance to these bodies as it provides great insights for decision making. There is a growing interest in developing models and algorithms which can understand and predict the diffusion of information in social networks. These models study the spread of information at a network level by modelling their spread at a node level. This involves considerable computational and algorithmic challenges and leverages advancements made in the various domains such as computational statistics, machine learning and graph theory. The course will cover various problems related to information diffusion arising in social networks and presents models and algorithms useful to model such problems. The significance of these models and algorithms are not limited to social network but also in healthcare, transportation, biological network, and internet of things.


The objective of the course is to present problems arising from social networks and web, study various models, and introduce state-of-the-art algorithms to solve these problems. The course will look at various problems interesting to the academy and industry such as event detection, information propagation, latent network estimation, influence maximisation and control, social activity modelling, and rumour detection and tracking. The course will introduce to the participants models based on point processes, deep learning, Bayesian learning and topic models.


Since good amount of time in the course will be devoted to theoretical aspects of Social and Information Networks Analysis, the participants are expected to have understanding of basic probability, and general notion of algorithms.

Course Contents

The following topics will be covered as part of the course. For each topic, the lectures will cover the theoretical foundations of the concept and also practical aspects.
Applications and Problems : Event detection, Information diffusion, meme tracking, opinion dynamics, Information reliability, Influence maximisation, activity shaping, source identification.
Models and Algorithms : Cascade process, temporal point process, Bayesian learning, deep learning, and topic models.

Faculty Profile

Dr. Manuel Gomez Rodriguez is a faculty at Max Planck Institute for Software Systems. He develops machine learning and large-scale data mining methods for the analysis, modelling and control of large real-world networks and processes that take place over them. He is particularly interested in problems arising in the Web and social media and has received several recognitions for his research, including a Best Paper Award Honoroble Mention at WWW’17, an Outstanding Paper Award at NIPS’13 and a Best Research Paper Honorable Mention at KDD’10. He has served as an area chair in the recent NIPS conferences, and program committee member and reviewer for several prestigious conferences and journals in machine learning such as NIPS, ICML, KDD, AAAI, JMLR etc. He regularly publishes his work at these top machine learning and data mining conferences and journals. Dr. Manuel holds a PhD and MS in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid (Spain). You can find more information here.

Dr. Srijith P. K. is an Assistant Professor at the department of Computer Science and Engineering, IIT Hyderabad. He is interested in developing probabilistic machine learning and Bayesian nonparametric models to solve problems arising in various domains of data science.He has developed novel machine learning approaches to solve several social network and natural language processing problems and has published papers in top venues such as ACL, EMNLP, ECML etc. He has been a reviewer for premier NLP conferences such as EMNLP, NAACL etc. and has served as a program committee member for CODS’17. Prior to joining IITH, he worked on social network analysis as a post-doctoral researcher at the University of Melbourne and at the University of Sheffield. Dr. Srijith did his Ph.D. at the department of Computer Science and Automation, Indian Institute of Science, Bangalore. He holds a M.Tech degree in Computer Science from IIT Bombay and a B.Tech degree in Computer Science from NIT Calicut. More details are available here.